Book Image

Bioinformatics with Python Cookbook - Third Edition

By : Tiago Antao
Book Image

Bioinformatics with Python Cookbook - Third Edition

By: Tiago Antao

Overview of this book

Bioinformatics is an active research field that uses a range of simple-to-advanced computations to extract valuable information from biological data, and this book will show you how to manage these tasks using Python. This updated third edition of the Bioinformatics with Python Cookbook begins with a quick overview of the various tools and libraries in the Python ecosystem that will help you convert, analyze, and visualize biological datasets. Next, you'll cover key techniques for next-generation sequencing, single-cell analysis, genomics, metagenomics, population genetics, phylogenetics, and proteomics with the help of real-world examples. You'll learn how to work with important pipeline systems, such as Galaxy servers and Snakemake, and understand the various modules in Python for functional and asynchronous programming. This book will also help you explore topics such as SNP discovery using statistical approaches under high-performance computing frameworks, including Dask and Spark. In addition to this, you’ll explore the application of machine learning algorithms in bioinformatics. By the end of this bioinformatics Python book, you'll be equipped with the knowledge you need to implement the latest programming techniques and frameworks, empowering you to deal with bioinformatics data on every scale.
Table of Contents (15 chapters)

Preparing a dataset for analysis

Our starting point will be a VCF file (or equivalent) with calls made by a genotyper (Genome Analysis Toolkit (GATK) in our case), including annotations. As we will be filtering NGS data, we need reliable decision criteria to call a site. So, how do we get that information? Generally, we can’t, but if we need to do so, there are three basic approaches:

  • Using a more robust sequencing technology for comparison – for example, using Sanger sequencing to verify NGS datasets. This is cost-prohibitive and can only be done for a few loci.
  • Sequencing closely related individuals, for example, two parents and their offspring. In this case, we use Mendelian inheritance rules to decide whether a certain call is acceptable or not. This was the strategy used by both the Human Genome Project and the Anopheles gambiae 1000 Genomes project.
  • Finally, we can use simulations. This setup is not only quite complex but also of dubious reliability...